r - 当一组参数被粘贴()作为向量时，如何获得glm模型的R中的真实残差偏差和自由度

Question

我正在编写一个脚本(在 python 中，在 pypeR 中使用 R 部分)，因此我需要在 R 中使用一个函数来比较两个模型与 F 比率测试。

模型是这样的:

型号 1: Response ~ Predictor A + Predictor B + Predictor C.... + Predictor n 型号 2: Response ~ Predictor 1
一起预测变量A+B+...n化妆Predictor 1 ，所以在这里嵌套没有问题(相信我)。

当我通过Predictor A + Predictor B + Predictor C.... + Predictor n对于我创建的函数，我认为它将它们视为一个变量(因为自由度与 Model 2 的自由度相同)。也许这是因为我正在使用 paste() ?无论如何，模型 1 中预测变量的实际数量将在运行中发生变化(这就是为什么我需要它作为函数的原因)，所以我不确定除了使用 paste() 之外还有什么方法可以适应这一点。 .

请记住，粘贴实际上可能不是这里的问题；我只是想让人们知道我认为问题可能是。

对于如何获得 model 1 的真实残差和自由度，有什么建议吗？ ?它可以是一个黑客。例如，我只是减去 length(vector of predictors) - 1来获得自由度。我不知道残留偏差的类似黑客会是什么。

这是函数和示例实例:

make_and_compare_models <- function(fitness_trait_name, data_frame_name, vector_for_multiple_regression, predictor_for_single_regression, fam){
    fit1<-glm(formula=as.formula(paste(fitness_trait_name,"~", paste(vector_for_multiple_regression, sep="+"))), family=fam, data=data_frame_name)
    #print ('length of vector of predictors')
    additional.degrees.of.freedom.fit1<-length(vector_for_multiple_regression)-1 ##the paste above prevents R from recognizing all of the vectors as separate predictors. This -1 gives you the difference in parameter number between the two models.
    print ("summary fit 1")
    print(summary(fit1))
    dev1<-(fit1$deviance)
    print ('residual deviance of fit1')
    print (dev1)
    print(fit1$df.residual)

    ##this is how I'd correct for degrees of freedom
    #df1=fit1$df.residual-additional.degrees.of.freedom.fit1
    #fit1$df.residual=df1

    ##if the old way
    df1=fit1$df.residual
    print(fit1$df.residual)
    print ('df1')
    print (df1)

    fit2<- glm(data=data_frame_name, formula=as.formula(paste(fitness_trait_name,"~",predictor_for_single_regression)), family=fam)

    print("summary fit 2")
    print(summary(fit2))
    print ("deviance of fit2")
    dev2<-(fit2$deviance)
    print(dev2)
    df2=fit2$df.residual
    print ('df2')
    print (df2)
    F.ratio<-((dev2-dev1)/(df2-df1))/(dev1/df1)
    print('F.ratio')
    print(F.ratio)
    new.p<-1-pf(F.ratio,abs(df1-df2),max(df2,df1))
    print('new.p')
    print(new.p)

}

data <- structure(list(ID = c(1L, 2L, 4L, 7L, 9L, 10L, 12L, 13L, 14L, 
15L, 16L, 17L, 18L, 20L, 21L, 22L, 23L, 24L, 25L, 27L, 28L, 29L, 
31L, 34L, 37L, 38L, 39L, 40L, 41L, 43L, 44L, 45L, 46L, 47L, 48L, 
49L, 52L, 55L, 56L, 59L, 60L, 61L, 62L, 63L, 65L, 66L, 67L, 68L, 
69L, 71L), QnWeight_initial = c(158L, 165L, 137L, 150L, 153L, 
137L, 158L, 163L, 159L, 151L, 145L, 144L, 157L, 144L, 133L, 148L, 
151L, 151L, 147L, 158L, 178L, 164L, 134L, 151L, 148L, 142L, 127L, 
179L, 162L, 150L, 151L, 153L, 163L, 155L, 163L, 170L, 149L, 165L, 
128L, 134L, 145L, 147L, 148L, 160L, 131L, 155L, 169L, 143L, 123L, 
151L), Survived_eclosion = c(0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 
1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), Days_wrkr_eclosion_minus20 = c(NA, 
1L, NA, 3L, 0L, 2L, 0L, 1L, 0L, 0L, 0L, 1L, NA, 0L, 7L, 1L, 0L, 
1L, 0L, 1L, 2L, 2L, NA, 2L, 3L, 2L, 2L, NA, 0L, 1L, NA, NA, 0L, 
0L, 0L, 0L, 3L, 3L, 3L, 1L, 0L, 2L, NA, 1L, 0L, 1L, 1L, 3L, 1L, 
2L), MLH = c(0.5, 0.666666667, 0.555555556, 0.25, 1, 0.5, 0.333333333, 
0.7, 0.5, 0.7, 0.5, 0.666666667, 0.375, 0.4, 0.5, 0.333333333, 
0.4, 0.375, 0.3, 0.5, 0.3, 0.2, 0.4, 0.875, 0.6, 0.4, 0.222222222, 
0.222222222, 0.6, 0.6, 0.3, 0.4, 0.714285714, 0.4, 0.3, 0.6, 
0.4, 0.7, 0.625, 0.555555556, 0.25, 0.5, 0.5, 0.6, 0.25, 0.428571429, 
0.3, 0.25, 0.375, 0.555555556), Acon5 = c(0.35387674, 0.35387674, 
0.35387674, 0.35387674, 0.35387674, 0.35387674, 0.35387674, 0, 
0, 1, 0, 1, 0.35387674, 0, 0, 0.35387674, 1, 1, 0, 0, 0, 1, 0, 
0.35387674, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 
0, 0, 1, 0, 0, 0, 1, 0, 0.35387674), Baez = c(1, 1, 1, 0.467836257, 
1, 1, 0, 0, 1, 1, 0, 0.467836257, 1, 0, 0, 0, 0, 1, 0, 0, 0, 
0, 0, 0.467836257, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 
1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1), C294 = c(0, 1, 0, 0, 1, 
0.582542694, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 
0, 1, 1, 0, 0, 0.582542694, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1), C316 = c(1, 1, 0, 0, 0.519685039, 
0.519685039, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0.519685039, 0, 
1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0.519685039, 1, 0, 1, 
1, 0, 0.519685039, 1, 0.519685039, 1, 1, 1, 0.519685039, 0.519685039, 
0, 0.519685039, 0.519685039, 0), i_120_PigTail = c(1, 1, 0, 1, 
0.631236443, 0.631236443, 1, 1, 1, 1, 1, 0, 0.631236443, 1, 1, 
1, 0, 0.631236443, 1, 1, 1, 0, 0, 1, 1, 1, 0.631236443, 0, 1, 
1, 0, 1, 0.631236443, 1, 0, 1, 0, 0, 1, 0.631236443, 0.631236443, 
0, 1, 0, 0.631236443, 0.631236443, 1, 0.631236443, 0.631236443, 
1), i129 = c(0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 
1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 
0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L), Jackstraw_PigTail = c(0L, 1L, 1L, 0L, 
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 
1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 
0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Neil_Young = c(0.529636711, 
0, 1, 0, 0.529636711, 0.529636711, 1, 1, 0, 1, 1, 1, 0, 0, 1, 
1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 
1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1), Ramble = c(0, 0, 0, 
0, 0.215163934, 0.215163934, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 
0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0.215163934, 0, 
0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0.215163934, 0, 0, 0, 0), Sol_18 = c(1, 
0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 
0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0.404669261, 
1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1)), .Names = c("ID", "QnWeight_initial", 
"Survived_eclosion", "Days_wrkr_eclosion_minus20", "MLH", "Acon5", 
"Baez", "C294", "C316", "i_120_PigTail", "i129", "Jackstraw_PigTail", 
"Neil_Young", "Ramble", "Sol_18"), class = "data.frame", row.names = c(NA, 
-50L))


make_and_compare_models("QnWeight_initial", data, c("Acon5","Baez","C294","C316","i_120_PigTail","i129","Jackstraw_PigTail","Neil_Young","Ramble","Sol_18"), "MLH", "gaussian")

Answer 1

也许我误解了这个问题，但是 anova将比较模型，您可以对其进行测试。我不确定您关于嵌套的声明(并将由您决定是否在这里做一些明智的事情)

comparemodels <- function(data, response, terms1, terms2, test, family = 'gaussian', ...) {
  f1 <- reformulate(terms1, response)
  f2 <- reformulate(terms2, response)
  m1 <- glm(f1, data = data, family = family)
  m2 <- glm(f2, data = data, family = family)
  compare <- anova(m1, m2, test = test)
  print(compare)

}

response <- 'QnWeight_initial'
t1 <- c("Acon5","Baez","C294","C316","i_120_PigTail","i129","Jackstraw_PigTail","Neil_Young","Ramble","Sol_18")
t2 <- 'MLH'
comparemodels(data, response,t1, t2,  test = 'F' )


Analysis of Deviance Table

Model 1: QnWeight_initial ~ Acon5 + Baez + C294 + C316 + i_120_PigTail + 
    i129 + Jackstraw_PigTail + Neil_Young + Ramble + Sol_18
Model 2: QnWeight_initial ~ MLH
  Resid. Df Resid. Dev Df Deviance      F Pr(>F)
1        39     7197.1                          
2        48     7614.1 -9  -417.08 0.2511 0.9837